diff --git a/is/kaggle/cv.py b/is/kaggle/cv.py
index acb1d68..57a9417 100644
--- a/is/kaggle/cv.py
+++ b/is/kaggle/cv.py
@@ -33,7 +33,6 @@
             train=data_train[i][data_train[i]['ID']==cleanData.iloc[j]['ID']]
             train['Weight']=float('NaN')
             data_train[i][data_train[i]['ID']==cleanData.iloc[j]['ID']]=train
-            print(data_train[i][data_train[i]['ID']==cleanData.iloc[j]['ID']]['Weight'])
     return (data_train,data_test)
 
 data_train, data_test=cv()
@@ -46,15 +45,19 @@
         test_value=np.float64(data_test[i].loc['Weight'])
         #no better idea...
         pred_value=predictions.iloc[int(data_test[i].loc['ID'])-1][predictedWeight]
-        print("test:",test_value)
-        print("pred:",pred_value)
         error+= (test_value - pred_value)**2
-        print(error)
     return(np.sqrt(error/n))
 
 #1st example
+rmse=[]
+sum=0
+n=0
 for i in range(10):
     data_train[i]['WeightInter'] = data_train[i]['Weight'].interpolate()
-    print(data_train[i])
-    print("RMSE(",i,"):",evaluate(data_train[i], data_test[i],'WeightInter'))
+    rmse.append(evaluate(data_train[i], data_test[i],'WeightInter'))
+    if(~np.isnan(rmse[i])):
+        n+=1
+        sum+=rmse[i]
+    print("RMSE(",i,"):",rmse[i])
 
+print("Mean RSME:",sum/n)